Automatic action responses

ABSTRACT

Systems and methods are provided that automatically process message input and provide action responses according to the processing results. The automatic action response system may leverage at least one machine-learning algorithm that is trained using a dataset. The provided action responses may comprise of default action responses and/or intelligent action responses that are based at least in part on prior conversational data between a user and a sender. Some intelligent action responses may include text-based replies, which eliminate the need for a user to type a reply on a device screen, thereby saving previous time, conserving device battery life, and preserving the integrity of the device hardware. A portion of a message may be highlighted manually by a user or automatically by the action response system to initiate the automatic action response system. In this way, a more efficient and productive user experience across various devices and applications is achieved.

BACKGROUND

Responding to electronic messages can be tedious and time-consuming.Specifically, replying to messages and posts on electronic mailapplications, text messaging applications, discussion forums, blogs,etc., exhaust precious amounts of time in the day-to-day lives of manyusers and consume exorbitant amounts of technical resources, includingdevice battery life and memory storage. Furthermore, by continuouslyusing a keyboard, touchpad, or similar input mechanism for replying to amessage, the input hardware (e.g., keyboard and mouse) will inevitablydeteriorate over time. The average American worker spends an estimated55% of their workday on tasks outside of their job role, includingemail, meetings, administrative tasks, and interruptions. In the samevain, it is estimated that 14% of the average workday is spent usingemail—nearly one-seventh of all time spent in the office. The 14% figureis even higher, e.g., 25%, of the workforce who receive more than 100emails every day.

In many instances, users often recycle the same reply messages. However,the majority of current messaging applications require that the userre-input that same reply message to each subsequent message thatwarrants the same reply. No current application provides the user with aset of automatically generated replies and/or actions across multipleapplications. Rather, the user is usually required to manually enter areply or take an action in response to the received message, rather thanallow a machine-learning algorithm to provide a set of suggested repliesor actions. As a result, excessive amounts of time and resources areconsumed during this process of monotonously taking actions (e.g.,replying, attaching a document, creating a calendar event) in responseto a received message.

The more time it takes to respond to logistical e-mails and recurrenttext messages from a friend, the less time users spend in the realworld. Conversely, the less time a user spends typing “See you soon” toa friend, the more time the user will have to actually enjoy the walkdown the street to meet that friend. Spending less time responding toeveryday messages via text messaging applications, blog posts,discussion forums, etc., inevitably leads to conserving significantlymore battery life in a user's device. Additionally, spending less timetyping on or touching an electronic device significantly decreases theamount of hardware deterioration of that device over time.Unfortunately, no current solutions exist to help curb these everydayrepetitious processes that consumer valuable time and resources.

It is with respect to these and other general considerations thatexample aspects, systems, and methods have been described. Also,although relatively specific problems have been discussed, it should beunderstood that the examples should not be limited to solving thespecific problems identified in the background.

SUMMARY

Providing users with automatic suggested responses and actions willallow users to be more productive throughout the day, as they will notbe wasting precious minutes typing out responses to routine messages,such as logistical emails or text messages. Increasing workerproductivity with smarter, faster replies and action responses is amethod for spending less time in the inbox and on other messagingapplications. Even the shortest emails require a handful of minutes, andthis precious time adds up quickly. By expediting routine replies andactions, users can take back this lost time and be more productive inthe workplace. More time will be spent on productive tasks and enjoyingthe real world, while less time will be spent on formulating and typingroutine replies or taking repetitive actions. Such efficiency andproductivity not only provides for a better overall user experience andhappier workers, but it also provides for better utilization oftechnological resources, such as local memory storage, battery life, andhardware sustainability.

A processor-implemented method of providing action responses isdisclosed herein. A portion of a message input from a computing devicemay be detected. The portion of the message input may then be tokenized.During the tokenizing step, the system may extract specific tokens fromthe message input data. A “token” may be characterized as any sequenceof characters. It may be a single character or punctuation mark, aphrase, a sentence, a paragraph, multiple paragraphs, or a combinationof the aforementioned forms. During the tokenization step, key wordsfrom the message input may be isolated and associated with generaltopics that may be preloaded into a natural language processor. Thesetopics may be located in a preexisting matrix of data where certaintokens are associated with certain topics. After the tokenization step,one or more features may be extracted from the message input.Additionally, at least one domain classifier may be applied to themessage input. The portion of the message input may then be convertedinto a semantic representation based on the determination of the domainclassifier. An action response may then be determined according to themessage input, the extracted features, the domain classification, andother data associated with the message input. The action response maythen be automatically provided. The action response may be a pop-up nextto a portion of text on a screen. In some example aspects, the actionresponse may be an automatic underlining or highlighting of a portion oftext. Other examples of action responses are disclosed herein. Finally,the portion of the message input, the extracted features, domainclassifications, action response(s), and any other associated data maybe stored in a database. Future cycles of providing action responses mayrely on this stored data to generate more accurate and personalizedaction responses.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTIONS OF THE DRAWINGS

FIG. 1 illustrates an example of a distributed system for receiving andstoring data related to providing intelligent action responses.

FIG. 2 is a block diagram illustrating a method for providingintelligent action responses according to processed message data.

FIG. 3 is a block diagram illustrating a method for processing messagedata to determine possible action responses.

FIG. 4A illustrates an example of an electronic mail application beforeintelligent highlights are applied.

FIG. 4B illustrates an example of an electronic mail application afterintelligent highlights are applied and possible intelligent actionresponses are displayed.

FIG. 4C illustrates an example of an electronic mail application afteran intelligent response is selected.

FIG. 5 illustrates an example of an electronic mail application withpossible intelligent action responses related to document attachments.

FIG. 6 illustrates an example of an electronic mail application withmultiple intelligent highlights and possible intelligent actionresponses.

FIG. 7A illustrates an example of a mobile text messaging applicationwith possible intelligent action responses.

FIG. 7B illustrates an example of a mobile text messaging applicationwith possible intelligent action responses.

FIG. 8 is a block diagram illustrating example physical components of acomputing device with which aspects of the disclosure may be practiced.

FIGS. 9A and 9B are simplified block diagrams of a mobile computingsystem in which aspects of the present disclosure may be practiced.

FIG. 10 is a simplified block diagram of a distributed computing systemin which aspects of the present disclosure may be practiced.

FIG. 11 illustrates a tablet computing device for executing one or moreaspects of the present disclosure.

DETAILED DESCRIPTIONS

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which are shown byway of illustrations or specific examples. These aspects may becombined, other aspects may be utilized, and structural changes may bemade without departing from the present disclosure. Example aspects maybe practiced as methods, systems, or devices. Accordingly, exampleaspects may take the form of a hardware implementation, a softwareimplementation, or an implementation combining software and hardwareaspects. The following detailed description is therefore not to be takenin a limiting sense, and the scope of the present disclosure is definedby the appended claims and their equivalents

The average day-to-day routine of a worker largely consists of checkingand responding to routine logistical emails. Not only do many peoplefind themselves re-typing the exact same reply to multiple emails, butthey also find themselves repeating mundane tasks within emailapplications, such as searching for and attaching pertinent documents.More broadly speaking, many people experience re-typing familiar textmessages to their friends and family. For example, the phrases “See yousoon” or “I love you” are common messages that are transmitted on adaily basis. In order to transmit these messages to friend or a lovedone, a user is often times required to type in that message into amessage dialog box each time. Consider an example of two people chattingwith each another. One of the conversation participants receives a textmessage from his daughter regarding the location of where he should pickher up after school. Unsurprisingly, the conversation participanttypically must pause the conversation with the other participant andtype out a message to his daughter. Alternatively, if the conversationparticipant was utilizing an action response system, the conversationparticipant may be able to simply tap on the received text message fromhis daughter, thereby highlighting the most relevant portions of thereceived message. Based on these intelligent highlights, the actionresponse system may provide suggested actions and/or responses to theconversation participant, which may be quickly selected with a singletap or touch. Thus, instead of interrupting a face-to-face conversationto unenthusiastically type out a frequently sent message about where theconversation participant will pick up his daughter, the conversationparticipant can simply select an intelligently suggested reply thatcontains the location where he will pick up his daughter and continuewith his conversation.

In another example, consider the precious minutes that are wasted whennavigating through folders to attach a document or graphic to anelectronic mail message or a discussion forum. If the received messagecontains enough information about which document the sender wishes toreceive, then an action response system may provide an intelligentlysuggested action of attaching the specifically requested document. Thus,a user will no longer have to navigate through folders and searchthrough a file system for desired attachments. The action responsesystem may automatically do that work for the user.

In addition to the abundant amounts of time that will be saved andultimately translated into higher efficiency and productivity, theaction response system may also significantly improve technologicalsystems. For example, the disclosed system may be able to generatenumerous intelligent replies and actions at a much faster rate than ahuman could generate manually. Such efficiencies may conserve electronicresources, like battery power, on the device side; and processing,memory and network resources on both the webpage/application providerside and the device side. Furthermore, utilizing a distributed system toprocess incoming message data and subsequently provide intelligentlysuggest action responses may allow memory to be more uniformlydistributed across devices involved in the system, ultimately resultingin faster processing speeds and a more dynamic allocation of memory.Also, automatically identifying relevant words, phrases, sentences,paragraphs, etc., and automatically generating associated intelligentaction responses reduces the amount of time users spend on theirelectronic devices, thereby preserving the longevity of the devicehardware and reducing the demand for customer service resources. Anautomatic action response system, like the system disclosed herein,results in improved customer satisfaction, efficiency, and productivitythroughout the day.

FIG. 1 illustrates an example of a distributed system for receiving andstoring data related to providing intelligent action responses.

A system that facilitates providing real-time detection of relevanttokens (i.e., portions of character sequences, such as keywords,phrases, sentences, and paragraphs, etc.) and automatically suggestssubsequent intelligent action responses may be run on an electronicdevice including, but not limited to, client devices such as a mobilephone 102, a tablet 104, and a personal computer 106. The disclosedsystem may receive message data from a messaging application, such as anelectronic mail application or an SMS application, running on a device.The disclosed system may then process that message data locally,remotely, or using a combination of both. During processing, thedisclosed system may rely on local and remote databases to generate themost appropriate intelligent action responses to provide back to theuser. This may be accomplished by utilizing local data stored in a localdatabase, remote database stored on servers 116, 118, and 120, or acombination of both.

For example, mobile phone 102 may utilize local database 110 and accessservers 116, 118, and/or 120 via network(s) 108 to process the messagedata and provide an appropriate intelligent action response. In otherexample aspects, tablet 104 may utilize local database 112 andnetwork(s) 108 to synchronize the relevant tokens extracted from theprocessed message data and the subsequent intelligent action responsesacross client devices and across all servers running the action responsesystem. For example, if the initial message input data is received ontablet 104, the message input data and subsequent intelligent actionresponse generation may be saved locally in database 112, but alsoshared with servers 116, 118, and/or 120 via the network(s) 108.

In other example aspects, the action response system may be employedlocally.

For instance, if the system servers 116, 118, and 120 are down, theaction response system may still operate on a client device, such asmobile device 102, tablet 104, and computer 106. In this case, a subsetof the trained dataset applicable to the client device type and at leasta client version of the machine-learning algorithms may be locallycached so as to automatically respond to relevant tokens highlighted inthe message data on the client device. The system servers 116, 118, and120 may be down for a variety of reasons, including but not limited to,power outages, network failures, operating system failures, programfailures, misconfigurations, and hardware deterioration.

As should be appreciated, the various methods, devices, components,etc., described with respect to FIG. 1 are not intended to limit systems100 to being performed by the particular components described.Accordingly, additional topology configurations may be used to practicethe methods and systems herein and/or components described may beexcluded without departing from the methods and systems disclosedherein.

FIG. 2 is a block diagram illustrating a method for providingintelligent action responses according to processed message data.

System 200 begins with receive message data operation 202. The messagedata may include, but is not limited to, the identity of the sender, thetime of day, the GPS locations of the sender and the user, grammaticalfeatures, semantic features, and syntactical features. Additionally, thesystem 200 may be able to acquire information regarding the sender'sdevice data and the user's device data, including operating environmentcharacteristics, battery life, hardware specifications, local files,third-party applications, and other relevant information that may beused to provide a more enjoyable and robust user experience with theaction response system. Other message data that mat be received atoperation 202 may include, but is not limited to, historicalconversation data between the sender and the user. For example, in anelectronic email message, system 200 may not only acquire dataassociated with the most recently transmitted message from the sender,but also the previous chain of sent and received electronic messagesbetween the sender and the user. Such comprehensive message data mayallow the action response system to provide more accurate andpersonalized suggested actions based on both the processed message dataand previously stored message data.

Additionally, system 200 may be able to receive message data in avariety of mediums, including, but not limited to, textual input, voiceinput, stylus input, and other input mechanisms. System 200 may receivenon-textual input and convert that input to text for processing througha natural language processor. Furthermore, system 200 may be able toreceive and process message data among a variety of different languagesand language types (e.g., right-to-left and left-to-right writtenlanguages). System 200 may receive all of or a portion of theaforementioned information related to message data at receive messagedata operation 202.

At process message data operation 204, system 200 may utilize a naturallanguage processor to identify the most relevant portions of the messagedata. For example, a sentence ending in a question mark may beidentified as a more pertinent part of the message data and processedaccordingly. In other example aspects, a question mark may cause thatsentence to receive a higher priority ranking during the process messagedata operation 204. A portion of the message data (e.g., a sentence)with a higher priority ranking may be highlighted first for the user andhave associated intelligent action responses before other portions ofthe message data are highlighted and populated with possible intelligentaction responses.

Additionally, process message data operation 204 may include comparingthe received message data from operation 202 with previously storedmessage data located in database 214. For example, as previouslymentioned, a portion of the message data received in operation 202 mayinclude the identity of the sender. During the process message dataoperation 204, the sender identity may be used to retrieve previouslystored data related to that specific sender, such as past conversationsbetween the user and the sender. Specifically, the historic data indatabase 214 may indicate that the user typically responds to theidentified sender in a particular way. Thus, rather than displaying abroad intelligent action response to the user, the action responsesystem may provide a more personalized and tailored response to theuser.

Furthermore, during process message data operation 204, the receivedmessage data may be parsed into “tokens,” which may be characterized asa sequence of characters. Tokens may be come in the form of a singlecharacter or punctuation mark (e.g., a question mark or exclamationpoint), a single key word, a phrase, a sentence, a paragraph, or acombination of the aforementioned forms. In order to parse the messagedata appropriately, the process message data operation 204 may comparecertain features of the message data against a set of already-importedexisting message data and/or historically saved message data to extractthe most pertinent aspects of the message data input. For example, a setof existing data may contain a prioritized list of tokens that indicatea question phrase. A sender asking a question most likely wants toreceive an answer to that question, so the question phrases may bemarked as high priority phrases. System 200 may determine whether or notthe message data contains any question phrases by comparing the messagedata input with the already-imported message data of frequently usedquestion tokens, such as “do you” or “can you” or “what.” If system 200finds a match at process message data operation 204 between thealready-imported message data from database 214 and the message datainput, then that word, phrase, sentence, etc. may eventually behighlighted for the user with a set of at least one action responsesattached. The process message data operation 204 is further described indetail in FIG. 3.

After the message data is processed at operation 204, system 200 thendetermines the appropriate action responses to associate with themessage data that may eventually be displayed to the user. During thedetermine action response options operation 206, the results from theprocessed message data may be analyzed to determine which actionresponse options are most appropriate. To make this determination,system 200 may compare an already-existing set of data from database 214that may contain a matrix of various message data tokens andcorresponding action response options. For example, a message data tokenthat was identified during the process message data operation 204 mayhave been a question mark. A preloaded matrix of message data tokens andassociated action response options may indicate that a question marktoken corresponds to the action response options of “yes” or “no.”However, further analysis of the processed message data may indicatethat the highlighted question from the message data may not be ayes/no-type question due to other message data tokens, such as tokensassociated with question words that do not elicit a yes/no answer, suchas “how,” “who,” “what,” etc. In the instances where a highlightedquestion is not a yes/no question, the determine action response optionsoperation 206 may analyze previous action responses that were selectedwith similar message data tokens. For example, a message data token maybe associated with the phrase “how are you.” Another token may beassociated with a question mark. Although the question mark token alonemay trigger a “yes” or “no” action response, the combination of thequestion mark token and the “how are you” token may trigger a morespecific action response, such as “I'm well” or “I'm OK, how are you?”Not only may boilerplate, preexisting sets of data be utilized indetermining the appropriate action responses to present to the user, butalso historical message data associated specifically with past actionresponses of the user may be utilized in determining the mostappropriate and personalized intelligent action responses to present tothe user.

After the appropriate action responses are determined at operation 206,the processed message data and the set of determined action responsesmay be saved at operation 212 and stored in database 214. Specifically,the various tokens that are associated with the message data input, thefeatures that were extracted from the message data, the domainclassifiers that were attached to the message data, the semanticdeterminations, and the set of possible action responses may all besaved at operation 212 so that future intelligent action responses maybecome more accurate and personalized. As a result of this constantinput of data, the machine-learning algorithms that determine theappropriate action responses become smarter and more personalized to theuser, ultimately saving more time for the user and conserving valuabletechnological resources.

Additionally, after the determine action response options operation 206,system 200 then determines whether to provide default action responsesat operation 208 or provide intelligent action responses at operation210. In some instances, a combination of both default action responsesand intelligent action responses may be provided to the user. Thedefault action responses may include, but are not limited to, “Quote”and “Highlight.” For example, “Quote” may refer to the action responseof copying a certain sentence or phrase from the received message into areply box, and upon using the “Quote” action response, a generic replymay automatically be suggested. Specifically, consider an example wherethe sentence was “Can you please merge my two code base accounts?” Ifthis sentence was highlighted manually by the user, then the user mayhave the default action response of “Quote.” By selecting “Quote,” thegeneric suggested replies may include “yes” or “no.” However, in someexample aspects the action response system 200 may automaticallyhighlight that sentence and provide more appropriate and intelligentreplies, such as “done!” or “No problem.” Specifically, if the userfrequently receives requests to merge code base accounts, system 200 mayhave historical data in database 214 that may be analyzed beforedetermining exactly which intelligent action responses to provide to theuser. If the user has frequently responded with “done!” in the past toquestions regarding merging code base accounts, then the action responsesystem may provide “done!” as the most appropriate and intelligentaction response.

In some example aspects, if system 200 is unable to determine whichintelligent action responses to provide to a user, the system 200 mayresort to providing default action responses in operation 208.Alternatively, if system 200 is able to determine at least oneintelligent action to provide to the user based on the processed messagedata and the historical data from database 214, then system 200 mayprovide intelligent action responses at operation 210.

After the action responses are presented to the user, if the userselects a suggest action response, that selection may be saved atoperation 216 and used in determining future intelligent actionresponses. In other example aspects, if a user declines to select asuggested action response (regardless if the action response is adefault action response from operation 208 or an intelligent actionresponse from operation 210), the user may respond to the message databy creating a manual reply or taking a manual action. This manual replyor action may be captured and stored in database 214 and utilized toconstruct future intelligent action responses specifically for the user.The previously processed message data from operation 204 may be comparedwith the manual input or action from the user that was saved atoperation 216. The machine-learning algorithms powering the actionresponse system may be able to generate new relationships among certainmessage data tokens, features, classifications, as well as manual inputsand actions. This relationship data may be leveraged in futureintelligent action responses, as the machine-learning algorithms maybecome smarter and more personalized.

System 200 may be initiated manually or automatically. In some exampleaspects, a user may select the portion of the message data to beanalyzed by the action response system. In other example aspects, theaction response system itself may automatically highlight certainportions of the message data and provide automatic action responses. Insome other example aspects, the action response system may analyze themessage data and process the message data. However, at determine actionresponse options operation 206, system 200 may conclude that no viableaction response systems currently exist. As a result, no portion of themessage data may be automatically highlighted for the user. However,even after the action response system reaches such a conclusion, a usermay still elect to highlight a portion of the message data and initiatethe action response system 200. Because the action response system 200had already analyzed the message data in this instance, the system 200may immediately jump to operation 208 and present the default actionresponses to the user. If the user selects one of the default actionresponses, that selection may be saved at save user action responseselection operation 216 and utilized in future intelligent actionresponse analyses. If the user declines to select one of the defaultaction responses and enters a manual reply or takes a manual action,that reply or action may also be stored at operation 216 and furtherutilized in future intelligent action response analyses.

Lastly, the action response system 200 may be implemented on a varietyof electronic devices including, but not limited to, a personalcomputer, a laptop computer, a mobile device, and a tablet. As discussedpreviously, system 200 may be run on a distributed system, comparinghistorical message and action response data across multiple devices andservers in order to provide the most accurate and intelligent set ofaction responses to the user.

As should be appreciated, the various methods, devices, components,etc., described with respect to FIG. 2 are not intended to limit systems200 to being performed by the particular components described.Accordingly, additional topology configurations may be used to practicethe methods and systems herein and/or components described may beexcluded without departing from the methods and systems disclosedherein.

FIG. 3 is a block diagram illustrating a method for processing messagedata to determine possible action responses.

As previously described, message data input 302 may consist of textualinput, speech input, handwritten/stylus input, and other various typesof input. For example, a message input 302 may consist of an electronicmessage in an electronic mail application, a text messaging application,or a discussion forum, among others. In other example aspects, themessage data input 302 may consist of a voice message that wastransmitted, for example, through a text messaging application. In yetother example aspects, a video message may have been pre-recorded andsent through an electronic mail application or a video chat application,such as Skype®. Regardless of the medium of the message data input 302,an intelligent action response or responses may be generated. Once theinput is received, the message data input 302 may be sent to the inputdecoder 304.

The input decoder 304 may determine if the input should be converted totext. If a message data input, such as a non-textual input like speechinput and/or handwriting input, should be converted to text, the inputdecoder 304 may transmit the message data input 302 to the convert totext operation 306, where the input may be converted to text forprocessing by the natural language processor 310. For example, themessage data input 302 may include a voicemail message. A voicemailmessage is a speech-based input and may need to be converted to text inorder for processing through the natural language processor 310. Avoicemail message may be received, and a user may desire to reply tothat voicemail message through a messaging application. The actionresponse system may convert the voicemail message to text at convert totext operation 306, thereby initiating the processing phase. Eventually,an action response may be provided to the user according to theprocessing of the voicemail message.

In some example aspects, the message data input 302 may not need to beconverted to text because the message data input is already in textualform when it is received by system 300. As a result, the text-basedmessage data may be transmitted from the input decoder 304 to thenatural language processor 310 for processing. As previously mentioned,in other example aspects, the message data may be in non-text format andbe transmitted to the convert to text operation 306. The convert to textoperation 306 may convert the message data input to text andsubsequently transmit the converted textual message data input to thenatural language processor 310 for further processing.

Once the text-based message data input is sent to the natural languageprocessor 310, the natural language processor 310 may parse the messagedata text and extract various semantic features and classifiers, amongother operations. The message data may be converted into semanticrepresentations that may be understood and processed by a machineutilizing machine-learning algorithms to intelligently disassemble themessage data and provide the most accurate and appropriate actionresponses.

In some example aspects, the natural language processor 310 may beginwith tokenization operation 312. The tokenization operation 312 mayextract specific tokens from the message input data. As previouslydescribed, a “token” may be characterized as any sequence of characters.It may be a single character or punctuation mark, a phrase, a sentence,a paragraph, multiple paragraphs, or a combination of the aforementionedforms. During the tokenization operation 312, key words from the messagedata input 302 may be isolated and associated with general topics thatmay be preloaded into the natural language processor. These topics maybe located in a preexisting matrix of data where certain tokens areassociated with certain topics. For example, a message data input mayinclude the following phrase: “Jim, do you want to play golf onWednesday afternoon?” The tokenization operation 312 may first associatea token with the sender. That token may subsequently be associated withpast conversations between the sender and the user, as well as processedmessage data and various action responses. The token phrase “do you” maybe associated with a non-yes/no question entity. The token word “golf”may be associated with an activity entity. In some example aspects, if auser frequently plays golf, a specific golf entity may be establishedthat is associated with any mention of “golf” or other derivative wordsand phrases (e.g., “tee,” “course,” “links,” etc.). In yet furtherexample aspects, the “golf” token may also be associated with GPSlocations of frequently visited golf courses by the user. The tokenphrase “Wednesday afternoon” may be associated with a calendar evententity. After tokenization operation 312, some possible action responsesthat may be presented to the user include, but are not limited to, anaction button to create a calendar event, a reply of “Absolutely!”, anaction button to a third-party golf application, an action button to amaps application displaying nearby golf courses, and a reply of “I'msorry, but I'm busy that day.”

After the message data input 302 is processed through the tokenizationoperation 312, the message data input may then be analyzed by thefeature extraction component 314. The feature extraction component mayextract lexical features 324 and contextual features 320 from themessage data input 302 for use by the domain classification component316. The lexical features 324 that may be analyzed in the featureextraction component 314 may include, but are not limited to, wordn-grams 322. A word n-gram is a contiguous sequence of n words from agiven sequence of text. As should be appreciated, analyzing word n-gramsmay allow for a deeper understanding of the message data input andtherefore provide more accurate and intelligent action responses to theuser. The machine-learning algorithms may be able to compare thousandsof n-grams, lexical features, and contextual features in a matter ofseconds to extract the relevant features of the message data. Such rapidcomparisons are impossible to employ manually. The contextual features320 that may be analyzed by the feature extraction component 314 mayinclude, but are not limited to, a top context and an average context. Atop context may be a context that is determined by comparing the topicsand key words of the message data input with a set of preloadedcontextual cues. An average context may be a context that is determinedby comparing the topics and key words of historical processed messagedata, historical action responses, manual inputs, manual actions, publicsocial media profiles, and other data. The feature extraction component314 may also skip contextually insignificant message data when analyzingthe textual input. For example, a token may be associated with articles,such as “a” and “an.” However, because articles are typicallyinsignificant in the English language, the feature extraction component314 may ignore these article tokens.

After the feature extraction component 314 extracts the pertinentlexical features 324 and contextual features 320 of the message datainput, the message data may be transmitted to the domain classificationcomponent 316. The domain classification component 316 analyzes thelexical features 324 and the contextual features 320 that werepreviously extracted. The domain classification component 316 analyzesthe lexical features 324 and the contextual features 320 that werepreviously extracted from the feature extraction component 314. Theselexical and contextual features may be grouped into specific classifiersfor further analysis. The domain classification component 316 may alsoconsider statistical models 326 when determining the proper domain thatshould be selected for the possible action responses. In some exampleaspects, the domain classification component 316 may be trained using astatistical model or policy (i.e., prior knowledge, historical datasets)with previous message data inputs. For example, as previously mentioned,the word “golf” may be associated with a specific activity token.Additionally, the word “golf” may be associated with a broader domainclassification, such as a “sports” domain. Previous message data andassociated action responses related to the “sports” domain may beanalyzed at this step. Similarly, the word “golf” may be associated witha group domain. For example, if a user frequently plays golf with threeother friends, any message data associated with those three friends maybe analyzed at this step, as well.

After proper domain classifications are assigned to the message datainput at operation 316, the message data input may then be sent to thesemantic determination component 318. The semantic determinationcomponent converts the message data input into a domain-specificsemantic representation based on the domain that was assigned to themessage data by the domain classification component 316. The semanticdetermination component 318 may draw on specific sets of concepts andcategories from a semantic ontologies database 330 to further narrowdown the set of appropriate action responses to present to the user. Forexample, a user may receive an electronic message that says “Do you wantto grab dinner with me Friday night?” The phrase “grab dinner with meFriday night” may indicate that the sender desires to know whether theuser has availability on Friday night and to know whether the user wantsto grab dinner with the user on Friday night. The key words and phrasesof “dinner” and “Friday night” may have previously been assigned domainsby the domain classification component 316, and as a result, thesemantic determination component 318 may then determine that the senderspecifically desires to know whether the user has availability on Fridaynight according to the calendar data of the user and whether the userdesires to have dinner with the sender according to previousconversational message data between the user and the sender, forexample. Thus, system 300 may access the calendar data of the user,determine that the user already has an event scheduled for Friday night,and as a result, eliminates any action response option of “Yes” or“Absolutely” or “Count me in!” etc.

In other example aspects, the semantic determination component 318 mayhave pre-defined semantic frames 328 associated with third-partyapplications. For example, a sender may transmit a voice message thatsays, “Do you want to see the Star Wars movie with me tonight?” Thesemantic determination component 318 may determine that the senderintends to see a movie with the user. Search information that may bepredefined by the semantic frames 328 may include, but is not limitedto, directors, actors, genres, release dates, show times, and ratings.Specifically, if the Star Wars movie had not yet been released, someappropriate intelligent action responses may be, “I'd love to, but thatmovie isn't out yet!” or “I thought Star Wars doesn't come out untilnext Friday.”

After the natural language processor 310 has completed its analysis ofthe message data input, the message data input may be transmitted to thedetermine possible action response operation 332. By funneling themessage data input through the natural language processor 310, many ofthe initial action responses may have been filtered out. Thus, determinepossible action responses operation 332 may be characterized as thefinal filter in determining which action responses to display to theuser and which order those action responses may be displayed to theuser. The determine possible action responses operation 332 may utilizea priority algorithm that considers not only the processed message datainput from the natural language processor 310, but also historicalaction response data from database 336, as well as default actionresponses from database 334. For example, a user may receive thefollowing message: “Can we go hiking this weekend?” After this messageis processed through the natural language processor 310, the determinepossible action response operation 332 may determine a priority rankingof the various intelligent action responses to display to the user. Datafrom the default action responses database 334 may provide actionresponses such as “Yes, I'd love to!” or “No, let's do something else.”More specific and intelligent action responses may be received andevaluated from the historical action response database 336. For example,the aggregation of historical message data and the public social profileof the user may indicate that the user enjoys hiking. However, morerecent message data and perhaps, public social profile data,specifically associated with the user's significant other may indicatethat the user's significant other had recently undergone knee surgery.Normally, the determine possible action response operation 332 mayprioritize the action response “Yes, I'd love to!” at the top of theaction response list. However, due to the newest data regarding theuser's significant other, the determine possible action responseoperation 332 may prioritize the response “I'd love to, but are youreally feeling well enough for that?” at the top and display such anaction response and similar responses first to the user.

In some example aspects, after the message data input is analyzed by thenatural language processor 310 and the determine possible actionresponse operation 332, a possible intelligent action response mayinvolve a third-party application. If a possible action responseimplicates a third-party application, the application synchronizer 338may be employed. the application synchronizer 338 may connect thepossible action responses with third-party applications. For example,consider the previous illustration, “Do you want to see the Star Warsmovie with me tonight?” In the instance that the Star Wars movie hadalready been released in theatres and the user had no calendar eventconflicts, the most intelligent action response to display to the usermay be an action button that would redirect the user to a third-partyapplication with show times for the Star Wars movie. In further exampleaspects, the system may aggregate nearby theatre show times andincorporate those show times into the possible action responses. Forexample, an intelligent action response that may be displayed to theuser may be, “Yes, how about the 9:10 pm show at the Pavilions UAtheatre?” By automatically pulling specific show time and theatrelocation data and integrating this data into the displayed actionresponses, the action response system eliminates the need for a user tonavigate among other applications to locate this show time and theatrelocation data and manually input the data into a subsequent message. Notonly does this save significant amounts of time and eliminate potentialtransmission errors (e.g., looking up a show time, but mistyping theshow time or theatre location), but it also conserves significantamounts of battery life for the user's device, since the user may notneed to navigate between multiple applications. Furthermore, the numberof query requests that the user may need to send and receive will alsodecrease, since the system intelligently collects the pertinentinformation from third-party applications without sending or receivingadditional queries over a network, which a user may carry out if theinformation was collected manually.

After the possible action responses are determined at operation 332 andthe action responses are potentially synchronized with third-partyapplications at operation 338, the action response manager 340 preparesthe action responses for display to the user. The action responsemanager 340 may also be responsible for saving the determined actionresponses for subsequent data analysis in further intelligent actionresponse processes. The action response manager 340 may store thedetermined action responses in the action response saver 344. The saver344 may be located locally on the device of the user, remotely (e.g.,server or distributed cloud environment), or on a combination of bothlocal and remote storage locations.

After any additional data has been gathered and the action responseshave been prepared for output by the action response manager 340, theoutput generator 342 generates a set of at least one action response(which may be a default action response or a personalized, intelligentaction response, or a combination of the two) to be displayed to theuser. The output provider 346 provides the set of action responses onthe screen of a device to the user. In some example aspects, the displayof the action response(s) may be a pop-up hovering over the pertinentportion of the message. In other example aspects, the display of theaction response(s) may be populated as a list in a reply textbox. In yetfurther example aspects, the display of the action response(s) may bepresented in a side bar on the side of a device screen or in a banner atthe top or bottom portions of a device screen. Additionally, multipledisplays of action responses may occur simultaneously. For example, oneportion of a message may have a set of action responses hovering over itin the form of a pop-up, while another portion of a message may have aset of action responses hovering over it in the form of a pop-up. Oncethe action responses are presented on the screen within the applicationor webpage, the displayed action responses may remain even if the usernavigates away from the application or webpage. For example, in anelectronic mail application, if a user activates the action responsesystem for one message, but then decides to navigate to another message,the user may then return to the previous message and see the displayedaction responses. In further example aspects, a user may navigate toanother message or application, activate the action response system onthe other message or application, select an appropriate action response,and subsequently return to the previous message and still see thedisplayed action responses.

As should be appreciated, the various methods, devices, components,etc., described with respect to FIG. 3 are not intended to limit system300 to being performed by the particular components described.Accordingly, additional topology configurations may be used to practicethe methods and systems herein and/or components described may beexcluded without departing from the methods and systems disclosedherein.

FIG. 4A illustrates an example of an electronic mail application beforeintelligent highlights are applied. As depicted, a personal computer401A is displaying an electronic mail application without anyintelligent highlights. A user may elect to activate an action responsesystem on the portion of the message designated by area 402A. Aspreviously mentioned, a user may manually highlight a portion of textusing external hardware, such as a keyboard and mouse, and activate theaction response system. Alternatively, if the action response system isconfigured to activate automatically within certain applications orperhaps, within any message-based activity on personal computer 401A,the action response system may automatically highlight the portion oftext designated by portion 402A and provide a set of action responses tothe user. As should be appreciated, the action response system mayidentify the message data initially without outwardly highlighting thetext. The action response system may process the message data anddetermine a set of appropriate action responses to display to the userinitially. Following the processing of the message data anddetermination of the action responses, the relevant portions of themessage may be highlighted for the user with the associated actionresponses displayed.

FIG. 4B illustrates an example of an electronic mail application afterintelligent highlights are applied and possible intelligent actionresponses are displayed. As depicted, personal computer 401B illustratesan electronic mail application with a portion of the message highlightedas designated by portion 402B. Portion 402B contains several phrasesthat may be tokenized as described in FIG. 3. For example, the phrase“can you” coupled with the punctuation mark “?” may indicate a yes/noquestion sentence. As previously mentioned, portions of message datawith such characteristics may be prioritized above othernon-interrogatory sentences. As a result, portion 402B may beautomatically highlighted by the action response system, and subsequentaction responses may be displayed.

As depicted, a pop-up action response box 404B contains two differentaction responses: Quote action response 406B and Highlight actionresponse 408B. Quote action response 406B may consist of the actionresponse system copying the portion of the message indicated by portion402B into a reply dialogue box and subsequently displaying anintelligent reply. The Highlight action response 408B may consist ofproviding feedback to the action response system about the portion ofmessage data designated by portion 402B. The Highlight action response408B may also allow a user to re-transmit the selected message portion402B through the action response system to ascertain whether or notother intelligent action responses may be provided.

In other example aspects, the pop-up action response box 404B may appearin a summary pane within the electronic mail application. For example,the pop-up action response box 404B may appear at position 414B in thelistview area of the electronic mail application. This may allow a userto select an intelligent action response or a default action responsewithout actually opening the e-mail message. The action response systemmay analyze message data of previous e-mail messages or unopened e-mailmessages and provide suggested action responses to the user. Thesuggested action responses, such as the action responses 406B and 408Blocated within action response box 404B, are not limited to the mostrecent message, but may be applied across previous messages, as well asmessages that have yet to be opened or read by the user.

FIG. 4C illustrates an example of an electronic mail application afteran intelligent response is selected. As depicted, personal computer 401Cillustrates an electronic mail application after an action response hasbeen applied based on the message portion 402C. Here, the user haselected to select the Quote action response 406C. The highlightedmessage portion 402C may then be copied to the area 410C within thereply dialogue box. Additionally, the action response system mayautomatically generate an intelligent response 412C. For example, a userwho frequently receives requests from senders to access a certain codebase may frequently reply to these requests with “Done!” The actionresponse system may capture this data, and the machine-learningalgorithms determining which action responses to display to the user maylearn from these frequent replies to subject matter regarding access tocode bases. As a result, the action response system may automaticallyprovide an intelligent reply 412C that the user may then elect totransmit back to the senders.

As should be appreciated, the various methods, devices, components,etc., described with respect to FIGS. 4A, 4B, and 4C are not intended tolimit system 400 to being performed by the particular componentsdescribed. Accordingly, additional topology configurations may be usedto practice the methods and systems herein and/or components describedmay be excluded without departing from the methods and systems disclosedherein.

FIG. 5 illustrates an example of an electronic mail application withpossible intelligent action responses related to document attachments.As depicted, personal computer 501 is running an electronic mailapplication with intelligent highlights and intelligent action responsesapplied. The highlighted message portion is designated by 502. Here, thetokens from message portion 502 include “send,” “Design Report,” and“yesterday's meeting.” Each of these tokens may all be analyzed throughthe action response system. Specifically, the token word “send” mayindicate that the sender desires an attachment of some kind. The phrase“Design Report” indicates a specific type of document, and the phrase“yesterday's meeting” implicates a calendar event that may be pulledfrom the user's calendar data. Aggregating the features that areassociated with this message may prompt the action response system tofirst refer to the user's calendar data and determine which meeting fromyesterday the sender may be referencing. The action response system maysearch the various meetings in the user's calendar data to find themeeting where both the user and Eddie Redd were participants. As aresult, the action response system may then use the start and end timeof that meeting to extract a dataset of local documents that were openedon a device belonging to a user between those two times. The metadata ofcertain documents may be searched to find the relevant open-file andclose-file timestamps. From this list of matching documents, the actionresponse system may search through the document names, specificallysearching for a document name that reflects the phrase “Design Report.”Upon a successful match, the action response system may associate thatspecific document with the attach action button 506 that is displayed asone of the three possible action responses, along with the Quote actionresponse 504 and the Highlight action response 508, within the actionresponse box 510. A user may then select attach action button 506, whichmay then automatically attach the requested Design Report documentreferenced in the message data according to message portion 502.

As should be appreciated, the various methods, devices, components,etc., described with respect to FIG. 5 are not intended to limit system500 to being performed by the particular components described.Accordingly, additional topology configurations may be used to practicethe methods and systems herein and/or components described may beexcluded without departing from the methods and systems disclosedherein.

FIG. 6 illustrates an example of an electronic mail application withmultiple intelligent highlights and possible intelligent actionresponses. As depicted, personal computer 601 is running an electronicmail application with multiple intelligent highlights and intelligentaction responses applied. The action response system may displaymultiple suggested action responses through multiple highlightedsentences, phrases, words, paragraphs, etc. For example, both messageportion 602 and message portion 604 may be highlighted simultaneously.As illustrated, message portion 604 is selected and currently displayingaction response box 614 with a Quote option 606, an Event actionresponse button 608, an Attach action response button 610, and aHighlight action response button 612. In other example aspects, multiplehighlighted portions may be selected and multiple action response boxesmay be displayed on the screen. In this example aspect illustrated inFIG. 6, the Event action response button 608 may be included within theaction response box 614 due to the results of processing the highlightedmessage portion 604. The results of processing highlighted portion 604may show that the tokens “our meeting,” “Monday at 10 am,” and “don'tforget,” are associated with a calendar entity. As a result, the actionresponse system may prepare a calendar event for the user according tothe tokens of message portion 604. If the user desires to add a calendarevent (e.g., Meeting at 10 am next Monday), the user may select Eventaction response button 608 from the action response box 614. The actionresponse system may synchronize with the calendar application associatedwith the user and add a calendar event. In other example aspects, theEvent action response button 608 may navigate the user to the calendarapplication, prompting the user for further input and details.

In yet other example aspects, the user may first select event actionresponse button 608 and create a new calendar event according to thedetails of message portion 604. The user may then subsequently selectthe Quote action response button 606, prompting the action responsesystem to provide a suggested reply (e.g., “See you at the meetingMonday!”). In other example aspects, after a user may select the Eventaction response button 608 and create a new event for the meeting, theuser may then select the Attach action response button 610 and attachthe newly created Event to a subsequent reply.

As should be appreciated, the various methods, devices, components,etc., described with respect to FIG. 6 are not intended to limit system600 to being performed by the particular components described.Accordingly, additional topology configurations may be used to practicethe methods and systems herein and/or components described may beexcluded without departing from the methods and systems disclosedherein.

FIG. 7A illustrates an example of a mobile text messaging applicationwith possible intelligent action responses. Mobile Device 701 is runninga text messaging application. As illustrated, the contact area 702Aindicates that Tony J. is the sender. In message 704A, Tony J. inquiresabout having lunch with the user on Tuesday. This message may beprocessed by the action response system. The processing results, asdescribed in more detail in FIG. 3, may produce a variety of suggestaction responses. As depicted inside the action response box 720,Highlight action response 714 and event action response 712 are theprovided action responses. Some possible tokens that may have beenextracted from message 704A include “lunch,” Tuesday,” and “?”.According to these tokens, the action response system may conclude thata suggested Event creation action response (event action response 712)may be the most intelligent and appropriate response to the message. Asmentioned previously, the action response system may be executed on anymessaging application, including, but not limited to, electronic mailapplications, text messaging applications, instant messagingapplications, discussion forums, and other messaging applications.

Additionally, message 706A indicates a Goodbye message from Tony J.Based on prior conversational history between the sender (Tony J.) andthe user and the possible other historical data (e.g., social mediaprofiles, etc.) related to the sender and the user, the most appropriatesuggest reply from the action response system may be message708A—“Peace, Bro.” For example, if Tony J. and the user are very closefriends, their prior conversation history may indicate that Tony J.often ends a conversation with the phrase “ttyl,” whereas the user mayoften end the conversation with “Peace, Bro.” As described in moredetail in FIG. 3, the type and substance of the reply message 708A maybe dependent on preset classifiers, in addition to personally tailoredconversational history between the sender and the user. Other dependentinformation may include aggregated data among many senders and users ofthe intelligent response system. If the user is content with thesuggested reply 708A, the user may select the send button 716A and forgotyping a reply. As a result, time is conserved, battery life is saved,and the hardware is preserved, among several other technologicalbenefits.

FIG. 7B illustrates an example of a mobile text messaging applicationwith possible intelligent action responses. In this example, the contactarea 702B indicates that the sender is Laura. For example, Laura may bethe significant other of the user. The user may want to respond toLaura's last message 706B. After highlighting message 706B and thereforeprompting the action response system to provide suggestions, an actionresponse box 718 may appear with a highlight action response 722 and areply message icon 720. As previously described, the highlight actionresponse 722 may prompt the action response system to generate moreaction responses than the ones displayed. It may also hide/show theaction response box 718 and apply/remove the highlighted aspect of thetext. The reply message icon 720 may generate a suggested reply in themessage box. As illustrated, a reply message 708B has beengenerated—“Love you too.” Before the action response system may displaymessage 708B, it may process message 706B and determine the appropriateaction responses, including the most intelligent replies. For example,Laura is the user's significant other, and therefore, instead of saying“Peace, Bro” (message 708A to Tony J.), the action response system maysuggest a more appropriate and intelligent response of “Love you too.”If the user is content with the suggested message 708B, the user mayelect to send the message by pressing send button 716B. As a result, theuser may not need to type out a reply message, thereby saving time andbattery life, as well as preserving the hardware of the mobile device701. The action response system may analyze the highlighted messageportions holistically, considering not only preset classifiers andtokens, but also historical conversational data between the sender andthe user, as well as other external data, such as social media profiles,GPS location data, and other information the action response system maycollect.

As should be appreciated, the various methods, devices, components,etc., described with respect to FIGS. 7A and 7B are not intended tolimit system 700 to being performed by the particular componentsdescribed. Accordingly, additional topology configurations may be usedto practice the methods and systems herein and/or components describedmay be excluded without departing from the methods and systems disclosedherein.

FIGS. 8-11 and the associated descriptions provide a discussion of avariety of operating environments in which aspects of the disclosure maybe practiced. However, the devices and systems illustrated and discussedwith respect to FIGS. 8-11 are for purposes of example and illustrationand are not limiting of a vast number of computing device configurationsthat may be utilized for practicing aspects of the disclosure, asdescribed herein.

FIG. 8 is a block diagram illustrating example physical components(e.g., hardware) of a computing device 800 with which aspects of thedisclosure may be practiced. The computing device components describedbelow may have computer-executable instructions for implementing anaction response manager 820 on a computing device (e.g., servercomputing device and/or client computing device). Thecomputer-executable instructions for an action response manager 820 canbe executed to implement the methods disclosed herein, including amethod of automatically processing a message input and providing atleast one intelligent action response according to the processingresults. In a basic configuration, the computing device 800 may includeat least one processing unit 802 and a system memory 804. Depending onthe configuration and type of computing device, the system memory 804may comprise, but is not limited to, volatile storage (e.g., randomaccess memory), non-volatile storage (e.g., read-only memory), flashmemory, or any combination of such memories. The system memory 804 mayinclude an operating system 805 and one or more program modules 806suitable for running an action response manager 820, such as one or morecomponents with regard to FIGS. 1, 2, and 3, and, in particular, aninput manager 811, a Natural Language Processor (NLP) manager 813, anaction response provider 815, and/or UX Component 817.

The operating system 805, for example, may be suitable for controllingthe operation of the computing device 800. Furthermore, embodiments ofthe disclosure may be practiced in conjunction with a graphics library,other operating systems, or any other application program and is notlimited to any particular application or system. This basicconfiguration is illustrated in FIG. 8 by those components within adashed line 808. The computing device 800 may have additional featuresor functionality. For example, the computing device 800 may also includeadditional data storage devices (removable and/or non-removable) suchas, for example, magnetic disks, optical disks, or tape. Such additionalstorage is illustrated in FIG. 8 by a removable storage device 809 and anon-removable storage device 810.

As stated above, a number of program modules and data files may bestored in the system memory 804. While executing on the processing unit802, the program modules 806 (e.g., action response manager 820) mayperform processes including, but not limited to, the aspects, asdescribed herein. Other program modules that may be used in accordancewith aspects of the present disclosure, and in particular forautomatically processing a message input and providing at least oneintelligent action response according to the processing results, mayinclude an input manager 811, an NLP manager 813, an action responseprovider 815, and/or UX Component 817, etc.

Furthermore, embodiments of the disclosure may be practiced in anelectrical circuit comprising discrete electronic elements, packaged orintegrated electronic chips containing logic gates, a circuit utilizinga microprocessor, or on a single chip containing electronic elements ormicroprocessors. For example, embodiments of the disclosure may bepracticed via a system-on-a-chip (SOC) where each or many of thecomponents illustrated in FIG. 8 may be integrated onto a singleintegrated circuit. Such an SOC device may include one or moreprocessing units, graphics units, communications units, systemvirtualization units and various application functionality all of whichare integrated (or “burned”) onto the chip substrate as a singleintegrated circuit. When operating via an SOC, the functionality,described herein, with respect to the capability of client to switchprotocols may be operated via application-specific logic integrated withother components of the computing device 800 on the single integratedcircuit (chip). Embodiments of the disclosure may also be practicedusing other technologies capable of performing logical operations suchas, for example, AND, OR, and NOT, including but not limited tomechanical, optical, fluidic, and quantum technologies. In addition,embodiments of the disclosure may be practiced within a general-purposecomputer or in any other circuits or systems.

The computing device 800 may also have one or more input device(s) 812such as a keyboard, a mouse, a pen, a sound or voice input device, atouch or swipe input device, etc. The output device(s) 814 such as adisplay, speakers, a printer, etc. may also be included. Theaforementioned devices are examples and others may be used. Thecomputing device 800 may include one or more communication connections816 allowing communications with other computing devices 850. Examplesof suitable communication connections 816 include, but are not limitedto, radio frequency (RF) transmitter, receiver, and/or transceivercircuitry; universal serial bus (USB), parallel, and/or serial ports.

The term computer readable media as used herein may include computerstorage media. Computer storage media may include volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information, such as computer readableinstructions, data structures, or program modules. The system memory804, the removable storage device 809, and the non-removable storagedevice 810 are all computer storage media examples (e.g., memorystorage). Computer storage media may include tangible storage media suchas RAM, ROM, electrically erasable read-only memory (EEPROM), flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other article ofmanufacture which can be used to store information and which can beaccessed by the computing device 800. Any such tangible computer storagemedia may be part of the computing device 800. Computer storage mediamay be non-transitory media that does not include a carrier wave orother propagated or modulated data signal.

Communication media may be embodied by computer readable instructions,data structures, program modules, or other data in a modulated datasignal, such as a carrier wave or other transport mechanism, andincludes any information delivery media. The term “modulated datasignal” may describe a signal that has one or more characteristics setor changed in such a manner as to encode information in the signal. Byway of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), infrared, andother wireless media.

FIGS. 9A and 9B illustrate a mobile computing device 900, for example, amobile telephone, a smart phone, wearable computer (such as a smartwatch or head-mounted display for virtual reality applications), atablet computer, a laptop computer, and the like, with which embodimentsof the disclosure may be practiced. In some aspects, the client may be amobile computing device. With reference to FIG. 9A, one aspect of amobile computing device 900 for implementing the aspects is illustrated.In a basic configuration, the mobile computing device 900 is a handheldcomputer having both input elements and output elements. The mobilecomputing device 900 typically includes a display 905 and one or moreinput buttons 910 that allow the user to enter information into themobile computing device 900. The display 905 of the mobile computingdevice 900 may also function as an input device (e.g., a touch screendisplay). If included, an optional side input element 915 allows furtheruser input. The side input element 915 may be a rotary switch, a button,or any other type of manual input element. In alternative aspects,mobile computing device 900 may incorporate more or less input elements.For example, the display 905 may not be a touch screen in someembodiments. In yet another alternative embodiment, the mobile computingdevice 900 is a portable phone system, such as a cellular phone. Themobile computing device 900 may also include an optional keypad 935.Optional keypad 935 may be a physical keypad or a “soft” keypadgenerated on the touch screen display. In various embodiments, theoutput elements include the display 905 for showing a graphical userinterface (GUI), a visual indicator 920 (e.g., a light emitting diode),and/or an audio transducer 925 (e.g., a speaker). In some aspects, themobile computing device 900 incorporates a vibration transducer forproviding the user with tactile feedback. In yet another aspect, themobile computing device 900 incorporates input and/or output ports, suchas an audio input (e.g., a microphone jack), an audio output (e.g., aheadphone jack), and a video output (e.g., a HDMI port) for sendingsignals to or receiving signals from an external device.

FIG. 9B is a block diagram illustrating the architecture of one aspectof a mobile computing device. That is, the mobile computing device 900can incorporate a system (e.g., an architecture) 902 to implement someaspects. In one embodiment, the system 902 is implemented as a “smartphone” capable of running one or more applications (e.g., browser,e-mail, calendaring, contact managers, messaging clients, games, andmedia clients/players). In some aspects, the system 902 is integrated asa computing device, such as an integrated personal digital assistant(PDA) and wireless phone.

One or more application programs 966 may be loaded into the memory 962and run on or in association with the operating system 964. Examples ofthe application programs include phone dialer programs, e-mail programs,personal information management (PIM) programs, word processingprograms, spreadsheet programs, Internet browser programs, messagingprograms, and so forth. The system 902 also includes a non-volatilestorage area 968 within the memory 962. The non-volatile storage area968 may be used to store persistent information that should not be lostif the system 902 is powered down. The application programs 966 may useand store information in the non-volatile storage area 968, such asemail or other messages used by an email application, and the like. Asynchronization application (not shown) also resides on the system 902and is programmed to interact with a corresponding synchronizationapplication resident on a host computer to keep the information storedin the non-volatile storage area 968 synchronized with correspondinginformation stored at the host computer. As should be appreciated, otherapplications may be loaded into the memory 962 and run on the mobilecomputing device 900, including the instructions for automaticallyprocessing a message input and providing at least one intelligent actionresponse according to the processing results as described herein (e.g.,input manager 811, NLP manager 813, action response provider 815, and/orUX Component 817, etc.).

The system 902 has a power supply 970, which may be implemented as oneor more batteries. The power supply 970 may further include an externalpower source, such as an AC adapter or a powered docking cradle thatsupplements or recharges the batteries. The system 902 may also includea radio interface layer 972 that performs the function of transmittingand receiving radio frequency communications. The radio interface layer972 facilitates wireless connectivity between the system 902 and the“outside world,” via a communications carrier or service provider.Transmissions to and from the radio interface layer 972 are conductedunder control of the operating system 964. In other words,communications received by the radio interface layer 972 may bedisseminated to the application programs 966 via the operating system964, and vice versa.

The visual indicator 920 may be used to provide visual notifications,and/or an audio interface 974 may be used for producing audiblenotifications via an audio transducer 925 (e.g., audio transducer 925illustrated in FIG. 9A). In the illustrated embodiment, the visualindicator 920 is a light emitting diode (LED) and the audio transducer925 may be a speaker. These devices may be directly coupled to the powersupply 970 so that when activated, they remain on for a durationdictated by the notification mechanism even though the processor 960 andother components might shut down for conserving battery power. The LEDmay be programmed to remain on indefinitely until the user takes actionto indicate the powered-on status of the device. The audio interface 974is used to provide audible signals to and receive audible signals fromthe user. For example, in addition to being coupled to the audiotransducer 925, the audio interface 974 may also be coupled to amicrophone to receive audible input, such as to facilitate a telephoneconversation. In accordance with embodiments of the present disclosure,the microphone may also serve as an audio sensor to facilitate controlof notifications, as will be described below. The system 902 may furtherinclude a video interface 976 that enables an operation of peripheraldevice 930 (e.g., on-board camera) to record still images, video stream,and the like.

A mobile computing device 900 implementing the system 902 may haveadditional features or functionality. For example, the mobile computingdevice 900 may also include additional data storage devices (removableand/or non-removable) such as, magnetic disks, optical disks, or tape.Such additional storage is illustrated in FIG. 9B by the non-volatilestorage area 968.

Data/information generated or captured by the mobile computing device900 and stored via the system 902 may be stored locally on the mobilecomputing device 900, as described above, or the data may be stored onany number of storage media that may be accessed by the device via theradio interface layer 972 or via a wired connection between the mobilecomputing device 900 and a separate computing device associated with themobile computing device 900, for example, a server computer in adistributed computing network, such as the Internet. As should beappreciated such data/information may be accessed via the mobilecomputing device 900 via the radio interface layer 972 or via adistributed computing network. Similarly, such data/information may bereadily transferred between computing devices for storage and useaccording to well-known data/information transfer and storage means,including electronic mail and collaborative data/information sharingsystems.

As should be appreciated, FIGS. 9A and 9B are described for purposes ofillustrating the present methods and systems and are not intended tolimit the disclosure to a particular sequence of steps or a particularcombination of hardware or software components.

FIG. 10 illustrates one aspect of the architecture of a system forprocessing data received at a computing system from a remote source,such as a general computing device 1004 (e.g., personal computer),tablet computing device 1006, or mobile computing device 1008, asdescribed above. Content displayed at server device 1002 may be storedin different communication channels or other storage types. For example,various documents may be stored using a directory service 1022, a webportal 1024, a mailbox service 1026, an instant messaging store 1028, ora social networking service 1030. The action response manager 1021 maybe employed by a client that communicates with server device 1002,and/or the action response manager 1020 may be employed by server device1002. The server device 1002 may provide data to and from a clientcomputing device such as a general computing device 1004, a tabletcomputing device 1006 and/or a mobile computing device 1008 (e.g., asmart phone) through a network 1015. By way of example, the computersystem described above with respect to FIGS. 1-9 may be embodied in ageneral computing device 1004 (e.g., personal computer), a tabletcomputing device 1006 and/or a mobile computing device 1008 (e.g., asmart phone). Any of these embodiments of the computing devices mayobtain content from the store 1016, in addition to receiving graphicaldata useable to either be pre-processed at a graphic-originating systemor post-processed at a receiving computing system.

As should be appreciated, FIG. 10 is described for purposes ofillustrating the present methods and systems and is not intended tolimit the disclosure to a particular sequence of steps or a particularcombination of hardware or software components.

FIG. 11 illustrates an exemplary tablet computing device 1100 that mayexecute one or more aspects disclosed herein. In addition, the aspectsand functionalities described herein may operate over distributedsystems (e.g., cloud-based computing systems), where applicationfunctionality, memory, data storage and retrieval and various processingfunctions may be operated remotely from each other over a distributedcomputing network, such as the Internet or an intranet. User interfacesand information of various types may be displayed via on-board computingdevice displays or via remote display units associated with one or morecomputing devices. For example, user interfaces and information ofvarious types may be displayed and interacted with on a wall surfaceonto which user interfaces and information of various types areprojected. Interaction with the multitude of computing systems withwhich embodiments of the invention may be practiced include, keystrokeentry, touch screen entry, voice or other audio entry, gesture entrywhere an associated computing device is equipped with detection (e.g.,camera) functionality for capturing and interpreting user gestures forcontrolling the functionality of the computing device, and the like.

As should be appreciated, FIG. 11 is described for purposes ofillustrating the present methods and systems and is not intended tolimit the disclosure to a particular sequence of steps or a particularcombination of hardware or software components.

The embodiments of the invention described herein are implemented aslogical steps in one or more computer systems. The logical operations ofthe present invention are implemented (1) as a sequence ofprocessor-implemented steps executing in one or more computer systemsand (2) as interconnected machine or circuit modules within one or morecomputer systems. The implementation is a matter of choice, dependent onthe performance requirements of the computer system implementing theinvention. Accordingly, the logical operations making up the embodimentsof the invention described herein are referred to variously asoperations, steps, objects, or modules. Furthermore, it should beunderstood that logical operations may be performed in any order, unlessexplicitly claimed otherwise or a specific order is inherentlynecessitated by the claim language.

The above specification, examples, and data provide a completedescription of the structure and use of exemplary embodiments of theinvention. Since many embodiments of the invention can be made withoutdeparting from the spirit and scope of the invention, the inventionresides in the claims hereinafter appended. Furthermore, structuralfeatures of the different embodiments may be combined in yet anotherembodiment without departing from the recited claims.

What is claimed is:
 1. A processor-implemented method of providingaction responses, comprising: detecting at least one portion of amessage input from a computing device; tokenizing the at least oneportion of the message input; extracting at least one feature from theat least one portion of the message input; determining at least onedomain classifier to apply to the at least one portion of the messageinput; converting at least one part of the at least one portion of themessage input into a semantic representation based on the determinationof the at least one domain classifier; determining at least one actionresponse corresponding to the at least one portion of the message input;automatically providing the at least one action response correspondingto the at least one portion of the message input; and updating at leastone database with the at least one portion of the message input, the atleast one feature, the at least one domain classifier, and the at leastone action response.
 2. The method of claim 1, wherein tokenizing the atleast one portion of the message input is based at least in part on apreset database of tokens.
 3. The method of claim 1, wherein determiningat least one domain classifier to apply to the at least one portion ofthe message input is based at least in part on a preset database ofdomain classifiers.
 4. The method of claim 1, wherein determining atleast one domain classifier to apply to the at least one portion of themessage input is based at least in part on past conversational data. 5.The method of claim 1, wherein determining the at least one actionresponse is based at least in part on a preset database of actionresponses.
 6. The method of claim 1, wherein determining the at leastone action response is based at least in part on identifying an actionresponse corresponding to past conversational data.
 7. The method ofclaim 1, wherein determining the at least one action response is basedat least in part on a public profile.
 8. The method of claim 6, whereinthe action response corresponding to past conversational data wasretrieved from the at least one database.
 9. The method of claim 1,wherein the at least one portion of the message input is text-based. 10.The method of claim 1, wherein the at least one portion of the messageinput is speech-based.
 11. The method of claim 1, wherein the at leastone database is associated with a machine-learning algorithm.
 12. Themethod of claim 1, wherein determining the at least one action responsecorresponding to the at least one portion of the message input is basedat least in part on identifying at least one third-party application.13. The method of claim 12, wherein the at least one action responseincludes at least one element corresponding to the at least onethird-party application.
 14. A computer device comprising: at least oneprocessing unit; at least one memory storing processor-executableinstructions that when executed by the at least one processing unitcause the computing device to: detect at least one portion of a messageinput from a computing device; tokenize the at least one portion of themessage input; extract at least one feature from the at least oneportion of the message input; determine at least one domain classifierto apply to the at least one portion of the message input; convert atleast one part of the at least one portion of the message input into asemantic representation based on the determination of the at least onedomain classifier; determine at least one action response correspondingto the at least one portion of the message input; automatically providethe at least one action response corresponding to the at least oneportion of the message input; and update at least one machine-learningdatabase with the at least one portion of the message input, the atleast one feature, the at least one domain classifier, and the at leastone action response.
 15. The computing device of claim 14, wherein theat least one action response is a default action response according tothe at least one machine-learning database.
 16. The computing device ofclaim 14, wherein the at least one action response is an intelligentaction response according to the at least one machine-learning database,wherein the intelligent action response is based at least in part onprior conversational data.
 17. The computing device of claim 14, whereinthe at least one machine-learning database is stored locally on a devicenot connected to a network.
 18. The computing device of claim 14,wherein the at least one portion of the message input corresponds to adiscussion forum.
 19. The computing device of claim 14, wherein the atleast one portion of the message input corresponds to at least oneportion of a voicemail message.
 20. A processor-readable storage mediumstoring instructions for executing on one or more processors of acomputing device, a method of providing action responses, the methodcomprising: detecting at least one portion of a message input from acomputing device; tokenizing the at least one portion of the messageinput; extracting at least one feature from the at least one portion ofthe message input; determining at least one domain classifier to applyto the at least one portion of the message input; converting at leastone part of the at least one portion of the message input into asemantic representation based on the determination of the at least onedomain classifier; determining at least one action responsecorresponding to the at least one portion of the message input;automatically providing the at least one action response correspondingto the at least one portion of the message input; and updating at leastone machine-learning database with the at least one portion of themessage input, the at least one feature, the at least one domainclassifier, and the at least one action response.